US20250165799A1 - Training data generation apparatus, training data generation method and program - Google Patents

Training data generation apparatus, training data generation method and program Download PDF

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Publication number
US20250165799A1
US20250165799A1 US18/840,569 US202218840569A US2025165799A1 US 20250165799 A1 US20250165799 A1 US 20250165799A1 US 202218840569 A US202218840569 A US 202218840569A US 2025165799 A1 US2025165799 A1 US 2025165799A1
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learning data
generator
vector
learning
data generation
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Yoichi Matsuo
Keishiro Watanabe
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NTT Inc
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Nippon Telegraph and Telephone Corp
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/094—Adversarial learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0475—Generative networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks

Definitions

  • the present disclosure relates to a learning data generation apparatus, a learning data generation method, and a program.
  • NPL 1 and NPL 2 propose schemes for estimating an abnormal portion.
  • NPL 3 proposes a scheme for modeling a relationship between an abnormal portion and a change in data in an ICT system caused in the abnormal portion as a causal model by using a Bayesian network, and estimating the abnormal portion from data observed during abnormality.
  • NPL 4 proposes an abnormality factor identifying scheme by generating fault data by chaos engineering.
  • the first method is a method of defining and modeling an abnormal portion and a rule of a change in data in an ICT system caused by the abnormal portion based on knowledge or the like of an expert operator (for example, NPL 3).
  • the second method is a method of constructing a causal model from an abnormal portion during past abnormality and data at that time.
  • a causal model is constructed by one of the two methods and an abnormal portion is estimated.
  • the collected data can be used for modeling a Bayesian network or can be used for learning data such as a support-vector machine (SVM), and an abnormal portion and a factor can be estimated.
  • SVM support-vector machine
  • the two construction methods of the causal models in the studies of the related art have problems.
  • the second method has a problem that it is difficult to sufficiently collect data during an abnormality necessary to construct the causal model. This is because an ICT system generally rarely generates an abnormality, and even if an abnormality occurs, a recurrence prevention measure is taken so that the same abnormality does not occur again.
  • a causal model is constructed based only on past abnormalities, so that the causal model cannot cope with unknown abnormalities, and an abnormal portion cannot be estimated.
  • Chaotic engineering is likely to partially solve the problem that it is difficult to sufficiently collect data during abnormality necessary to construct a causal model, but cannot be said to suffice. This is because a wide variety of abnormalities occur in an ICT system, but chaos engineering is a method of intentionally inserting a fault, and thus only data related to abnormalities able to be conceived by humans can be obtained.
  • the present disclosure has been devised in view of the foregoing circumstances and provides a technique for generating data used to construct a model for estimating an abnormal portion.
  • x i is a k-dimensional vector representing past abnormal data.
  • k is the number of types of data, such as a traffic amount collected from the ICT system and a central processing unit (CPU) usage rate. That is, each x i represents any of various states such as a traffic amount and a CPU usage rate when the ICT system is abnormal.
  • N is the number of pieces of abnormal data.
  • Each x i may have a data value at a certain time as an element, or may have a statistical value such as an average of data values in a certain time duration as an element.
  • y 1 is an l-dimensional (where, l is a lower case letter of L) vector.
  • l denotes the number of apparatuses in the ICT system.
  • each element of y i corresponds to each apparatus in the ICT system.
  • the present invention is not limited thereto.
  • each element of y i corresponds to an I/F of an apparatus or a device built into the apparatus.
  • each element corresponds to a device built into the apparatus, it is possible to estimate an abnormal portion in units of devices.
  • y i is a one-hot vector in which only a j ⁇ 1, . . . , j ⁇ -th element corresponding to the abnormal portion is 1, and the other elements are 0.
  • the discriminator D ( ⁇ ; ⁇ D ) accepts the k-dimensional vector as an input and outputs a scalar value of 0 or 1.
  • One of the abnormal data x i actually collected from the ICT system or the data ⁇ circumflex over ( ) ⁇ x i generated by the generator G is input to the discriminator D ( ⁇ ; ⁇ D ), and it is determined whether x i or ⁇ circumflex over ( ) ⁇ x i is input.
  • the discriminator D ( ⁇ ; ⁇ D ) outputs 1 when it is determined that x i is input, and outputs 1 when it is determined that ⁇ circumflex over ( ) ⁇ x i is input.
  • the parameter ⁇ D is learned so that discrimination performance is enhanced.
  • a loss function L of the CGAN including the generator G ( ⁇ ; ⁇ G ) and the discriminator D ( ⁇ ; ⁇ D ) is shown in the following Formula (1).
  • E( ⁇ ) is an expected value and z is an m-dimensional vector generated at random. z is also called noise.
  • x ⁇ X and y ⁇ Y are abnormal portion data when abnormality occurs with regard to the abnormal data x ⁇ X.
  • cot (z, y) is an operation of combining z and y to generate an (m+l)-dimensional vector.
  • learning data is generated by the generator G ( ⁇ ; ⁇ G ) having the learned parameter ⁇ G .
  • the (m+l)-dimensional vector obtained by combining an m-dimensional vector z generated at random and an 1-dimensional vector y generated at random is input to the learned generator G ( ⁇ ; ⁇ G ), and the k-dimensional vector ⁇ circumflex over ( ) ⁇ x is obtained as an output.
  • learning data ( ⁇ circumflex over ( ) ⁇ x, y) for constructing a model for estimating an abnormal portion of the ICT system for example, the causal model modeled by a Bayesian network or the like, a machine learning model such as SVM
  • the l-dimensional vector y is, for example, a one-hot vector in which only the j-th vector is set to 1 at random due to a uniform distribution or the like.
  • FIG. 2 illustrates a hardware configuration example of the learning data generation apparatus 10 according to the embodiment.
  • the learning data generation apparatus 10 includes an input device 101 , a display device 102 , an external I/F 103 , a communication I/F 104 , a random access memory (RAM) 105 , a read only memory (ROM) 106 , an auxiliary storage device 107 , and a processor 108 .
  • the hardware is communicatively connected via a bus 109 .
  • the input device 101 is, for example, a keyboard, a mouse, a touch panel, various physical buttons, or the like.
  • the display device 102 is, for example, a display or a display panel.
  • the learning data generation apparatus 10 may not include at least one of the input device 101 and the display device 102 .
  • the external I/F 103 is an interface with an external device such as a recording medium 103 a .
  • the learning data generation apparatus 10 can perform reading and writing from and on the recording medium 103 a via the external I/F 103 .
  • Examples of the recording medium 103 a include a flexible disk, a compact disc (CD), a digital versatile disk (DVD), a secure digital (SD) memory card, and a Universal Serial Bus (USB) memory card.
  • the communication I/F 104 is an interface for connecting the learning data generation apparatus 10 to a communication network.
  • the RAM 105 is a volatile semiconductor memory (storage device) that temporarily stores programs and data.
  • the ROM 106 is a nonvolatile semiconductor memory (storage device) that can hold programs and data even when a power source is turned off.
  • the auxiliary storage device 107 is a storage device such as a hard disk drive (HDD) or a solid state drive (SSD). Examples of the processor 108 include various arithmetic devices such as a CPU and a graphics processing unit (GPU).
  • the learning data generation apparatus 10 can implement various types of processing which will be described below with the hardware configuration illustrated in FIG. 2 .
  • the hardware configuration illustrated in FIG. 2 is merely exemplary, and the hardware configuration of the learning data generation apparatus 10 is not limited thereto.
  • the learning data generation apparatus 10 may include a plurality of auxiliary storage devices 107 and a plurality of processors 108 , or may have various pieces of hardware other than the illustrated hardware.
  • FIG. 3 illustrates a functional configuration example of the learning data generation apparatus 10 according to the embodiment.
  • the learning data generation apparatus 10 includes an observation data collection unit 201 , a generation unit 202 , a discrimination unit 203 , a learning unit 204 , and an output unit 205 .
  • Each of these units is realized through, for example, processing executed by the processor 108 or the like according to one or more programs installed in the learning data generation apparatus 10 .
  • the learning data generation apparatus 10 according to the embodiment includes an observation data DB 206 .
  • the observation data DB 206 is realized by, for example, the auxiliary storage device 107 .
  • the observation data DB 206 may also be realized by, for example, a storage device connected to the learning data generation apparatus 10 via a communication network or the like.
  • the observation data collection unit 201 collects abnormal data x of the ICT system and abnormal portion data y when abnormality occurs.
  • the abnormal data x and the abnormal portion data y are stored in the observation data DB 206 . Accordingly, the data set X formed by the abnormal data x and the data set Y formed by the abnormal portion data y are stored in the observation data DB 206 .
  • the generation unit 202 is realized by the generator G ( ⁇ ; ⁇ G ), and accepts an (m+l)-dimensional vector as an input and outputs a k-dimensional vector.
  • the discrimination unit 203 is realized by the discriminator D ( ⁇ ; ⁇ D ), and accepts the k-dimensional vector as an input and outputs a scalar value of 0 or 1.
  • the learning data generation apparatus 10 can learn the CGAN using the observation data (x, y) during abnormality of the ICT system and can generate the learning data ( ⁇ circumflex over ( ) ⁇ x, y) for constructing a model for estimating an abnormal portion of the ICT system by the generator G included in the CGAN. Accordingly, a sufficient amount of learning data necessary to construct the model can be obtained.
  • the generator G accepts a vector in which a vector z generated at random and a one-hot vector y generated at random are combined as an input, and generates abnormal data ⁇ circumflex over ( ) ⁇ x. Therefore, for example, abnormal data which is difficult to obtain in the chaos engineering can also be generated. Accordingly, by using the learning data generated by the learning data generation apparatus 10 according to the embodiment, it is possible to construct a model capable of estimating an abnormal portion with high accuracy.

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